Previous research through the Model Spinal Cord Injury Systems and a study of British participants with spinal cord injury (SCI) has documented a high prevalence of many deaths after SCI that are preventable (e.g., suicides, septicemia). Research by the principal investigator has indicated that aspects of general adaptation after SCI, including being more active and satisfied with life, are associated with a greater length of survival after SCI (suggesting that poor adaptation underlies many of these types of deaths). Unfortunately, no research has identified the types of stable psychological traits or specific behaviors that either protect the individual for early mortality or that are associated with a greater risk of mortality. Identifying these types of traits and behaviors would establish targets for intervention and prevention that ultimately could lead to greater longevity among people with SCI by reducing the number of deaths due to preventable causes (i.e., those that results from risk behaviors). The purpose of the current proposal is to perform a prospective study of mortality that would identify associations between several sets of factors and early mortality after SCI. Unlike previous research, this study would link traits, behaviors, and health status factors directly to risk for mortality, while maintaining the classification scheme for causes of deaths using the scheme developed with the model SCI systems. The mortality status of 1,391 participants will be determined, all of whom completed a large-scale health survey four years earlier. Biological factors, psychological traits, risk and protective behaviors, and health status and secondary conditions will be assessed. Mortality status will be determined using two national databases - the National Death Index and records from the Social Security Administration. Cox proportional hazard models will be used to identify the significance of individual predictors to compare the importance of classes of predictors, and to identify the optimal predictors of mortality using all predictors. Risk profiles will be generated to identify individuals at greatest risk for early mortality. Separate analyses and risk profiles will be generated based on injury severity, gender and race/ethnicity.